1. Field of the Invention
The present invention generally relates to computer-implemented methods and systems for classifying defects on a specimen. Certain embodiments relate to a computer-implemented method that includes allowing a user to assign a classification to defect groups to which individual defects detected on a specimen are assigned based on one or more characteristics of the individual defects.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Wafer inspection systems often find thousands of anomalies (commonly called “events” or “defects”) on each wafer. Defects may have many forms such as structural flaws, process residues, and external contamination that may occur during semiconductor wafer fabrication. As processes for making wafers evolve, the defect types that are of interest change. The importance of a defect depends on several factors such as appearance and other characteristics such as size and location.
Classifying defects found on wafers and other specimens has, therefore, become increasingly important in order to determine what kinds of defects are present on the wafers in addition to distinguishing the defect types of interest from other defect types. Classifying defects may also include determining if defects are actual defects or nuisance defects. Nuisance defects can be generally defined as a portion of a specimen that appears to be a defect during inspection but is not actually defective.
Generally, classification is performed after wafer inspection has been completed. In addition, classification is usually performed during defect review or after defect review. Defect review generally involves using a different tool than that which was used for inspection. For instance, defect detection is usually performed using an optical inspection tool while defect review is usually performed using an electron beam review tool. However, defect review may be performed using an optical review tool that has a higher magnification or resolution than the optical inspection tool. In this manner, the defect review tool can be used to gain more detailed information about possible defects. As such, the information generated by the defect review tool may be particularly suitable for defect classification.
In the past, defect classification has been performed in several different ways. For example, defect classification can be performed completely manually by an operator. Typically, the operator is presented with defect images or other defect data for each defect sequentially one at a time. The operator then assigns a classification (e.g., pit, particle, etc.) to the defect based on defect appearance and possibly other characteristics (e.g., roughness). Experienced operators can be fairly efficient at classifying defects on wafers. However, manual defect classification performed by even the most skilled and experienced operators takes an unacceptably long time. For instance, the operator typically classifies individual defects one at a time. In this manner, regardless of how skilled the operator is, the time that is needed to perform classification will necessarily depend on how many defects were detected on the wafer. Furthermore, reviewing many defect images or other data repetitively one after another will necessarily produce operator fatigue and loss of concentration. Therefore, even a skilled operator may mistakenly classify defects due to diminished alertness. Furthermore, it can be fairly expensive to employ an operator to review and classify defects particularly since manual defect classification as described above is so time intensive.
Since there are a fair number of disadvantages to currently used methods for manual defect classification, efforts have been made to automate the defect classification process. Several fully automatic defect classification (ADC) tools are now available. Typically, these tools use classification “recipes” to perform defect classification. A “recipe” can be generally defined as a set of instructions that define an operation to be performed by a tool and that are provided to and run on the tool upon request by a user. The recipes are typically generated using previous data about specific defect classes that may be assembled in a suitable database. In the simplest implementation, the ADC tool can then compare unknown defects to those included in the specific defect classes to determine which defect class the unknown defect is most like. Obviously, much more complicated algorithms can be used by the ADC tool to determine which of the defect classes the unknown defect most likely belongs to.
The concept of ADC is fairly simple. However, the implementation has proven to be fairly complex and difficult. For example, generating a suitable database for an ADC recipe usually involves locating a substantial number of each defect type on wafers using wafer inspection and manual defect classification, which may be performed as described above. The data for each defect of a particular type may then be combined into a suitable database. The defect data that is included in the database may be selected by the user. This set of representative defect data may be commonly referred to as a “training set.” Although a database generated as described above may be relatively accurate, generating the database is typically time consuming and expensive. In addition, since an ADC recipe tends to be accurate for only those defects that are fairly similar to those in the training set, ADC recipes may be useful only for substantially similar processes which tend to produce the same kinds of defects over time. Defects that are not sufficiently similar to those in the database may be incorrectly classified or not classified at all. Accordingly, ADC recipes usually cannot be used for different processes or different types of specimens, and therefore, many such recipes may be generated depending on the defects and specimens to be inspected. As such, the inflexibility of ADC recipes may increase the cost of ADC since each time a process or device is changed, the ADC recipe may need to be updated manually. In addition, the time and expense of generating many different ADC recipes may be substantially prohibitive.
Despite the drawbacks of the various types of defect classification methods and tools described above, defect classification will only increase in importance in semiconductor device manufacturing in the future. For example, defect classification can be used to identify problems with semiconductor fabrication processes. In addition, defect classification can be used to identify problems with semiconductor device designs. Therefore, since the results of defect classification may be used to make yield management decisions about semiconductor processes and designs, the accuracy of the defect classification may have a direct effect on the success of semiconductor manufacturing.
Accordingly, it may be advantageous to develop computer-implemented methods and systems for classifying defects on a specimen that are relatively inexpensive, quick, accurate, flexible, and easily account for unexpected defect types on many different types of wafers or other specimens.